Variable Admittance for pHRI: From Intuitive Unilateral Interaction to Optimal Bilateral Force Amplification

Labrecque, Pascal; Gosselin, Clément

doi:10.1016/j.rcim.2018.01.005

Cited by 30 publications

(14 citation statements)

References 13 publications

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“…In some pHRI applications, researchers have relied on amplification (attenuation) of human force to increase (decrease) the contribution of robot to the collaborative task [11], [27]- [29]. A well-known example in this group is exoskeletons [9], [30]- [32] which are designed to amplify human force.…”

Section: A Related Workmentioning

confidence: 99%

Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

Hamad

Aydın

Başdoğan

2021

IEEE Trans. Haptics

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The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts' reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.

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Section: A Related Workmentioning

confidence: 99%

Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

Hamad

Aydın

Başdoğan

2021

IEEE Trans. Haptics

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“…where λ(D) ∈ R denotes the minimum eigenvalue of D. To derive (23) we have further utilized the fact that kJ T T (ψ * )ζ ≥ 0. Hence, ( 21) is strictly output passive for all t ∈ [t 0 , τ ), with respect to v [61].…”

Section: The Closest Desired Posementioning

confidence: 99%

“…Hence, ( 21) is strictly output passive for all t ∈ [t 0 , τ ), with respect to v [61]. By completing the squares in (23), we get:…”

Section: The Closest Desired Posementioning

confidence: 99%

“…(ii) Employing LaSalle invariance principle for F x = 0 6 , it is easy to show that the state will converge to s d , from (23).…”

Section: The Closest Desired Posementioning

confidence: 99%

“…Moreover, human intention prediction is used either to adapt the target admittance [20], or to reshape the nominal path [21], [22] during pHRI. In [23], an automatic smooth transition between interaction modes is proposed, to provide precision and comfort during pHRI in free space, and enable force amplification when the robot is in contact with its environment.…”

Section: Introductionmentioning

confidence: 99%

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A Passive pHRI Controller for Assisting the User in Partially Known Tasks

et al. 2020

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In this work, a passive physical human-robot interaction (pHRI) controller is proposed to enhance pHRI performance in terms of precision, cognitive load and user effort, in cases partial knowledge of the task is available. Partial knowledge refers to a subspace of SE(3) determined by the desired task, generally mixing both position and orientation variables and which is mathematically approximated by parametric expressions. The proposed scheme, which utilizes the notion of virtual constraints and the prescribed performance control methodology, is proved to be passive with respect to the interaction force, while guaranteeing constraint satisfaction in all cases. The control scheme, is experimentally validated and compared with a dissipative control scheme utilizing a KUKA LWR4+ robot in a master-slave task; experiments also include an application to a robotic assembly case.

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